How to Build a Robust Java API Monitoring Stack with OpenTelemetry, Prometheus, and Grafana
Server-side errors, particularly the dreaded 5xx family, are silent killers of user experience and business reputation. When your Java API starts failing, you need to know immediately—not after customers start complaining. Relying on reactive measures is no longer a viable strategy. The key to maintaining a reliable service is a proactive monitoring and alerting system that catches issues the moment they arise.
Fortunately, building a powerful, enterprise-grade observability stack is more accessible than ever, thanks to a combination of open-source tools. By leveraging OpenTelemetry, Prometheus, and Grafana, you can create a comprehensive monitoring solution to track 5xx error rates, latency, and other critical metrics for your Java applications.
This guide will walk you through the essential components of this stack and how to configure them to keep your APIs healthy and your users happy.
Understanding the Modern Observability Stack
To effectively monitor your application, you need three key components working in harmony: a data collector, a storage and querying engine, and a visualization layer.
OpenTelemetry (The Collector): OpenTelemetry (OTel) is the new standard for application instrumentation. It provides a single, vendor-neutral set of APIs and libraries to collect telemetry data—metrics, logs, and traces—from your application. For Java developers, its most powerful feature is the auto-instrumentation agent. This allows you to gather a wealth of performance data from your application without changing a single line of code.
Prometheus (The Database & Alerter): Prometheus is a leading open-source time-series database designed for reliability and scalability. It periodically “scrapes” or pulls metrics from configured endpoints, such as the one exposed by OpenTelemetry. Its powerful query language, PromQL, allows you to slice, dice, and aggregate data in sophisticated ways. More importantly, Prometheus includes a built-in Alertmanager to define alerting rules and notify you via Slack, email, or other channels when things go wrong.
Grafana (The Visualization Layer): While Prometheus is great at storing data and firing alerts, Grafana is where you bring that data to life. Grafana is a premier visualization tool that connects to dozens of data sources, including Prometheus. It allows you to build rich, interactive dashboards to monitor API health in real-time. You can visualize everything from error rates and request throughput to JVM performance metrics, giving you a single pane of glass for your application’s status.
Setting Up Your 5xx Error Monitoring System: A Step-by-Step Guide
Implementing this stack involves instrumenting your application, configuring data scraping, and setting up alerts and dashboards.
Step 1: Instrument Your Java API with the OpenTelemetry Agent
The first step is to get your Java application to emit the necessary metrics. Thanks to the OpenTelemetry Java agent, this is surprisingly simple.
- Download the OpenTelemetry Java Agent JAR file.
- Attach the agent to your application by adding a single JVM argument at startup:
-javaagent:path/to/opentelemetry-javaagent.jar
That’s it. The agent will automatically instrument common frameworks like Spring Boot, Jakarta EE, and others, exposing a wide range of standard metrics, including HTTP request counts and durations, categorized by status code. By default, it exposes these metrics on a /metrics endpoint that Prometheus can access.
Step 2: Configure Prometheus to Scrape the Metrics
Next, you need to tell your Prometheus instance where to find your application’s metrics. This is done by adding a new scrape_config to your prometheus.yml configuration file.
This configuration tells Prometheus to periodically send an HTTP request to your application’s /metrics endpoint and store any new data it finds.
Step 3: Create a 5xx Error Rate Alert in Prometheus
This is where proactive monitoring comes into play. You can write a rule in Prometheus to calculate the percentage of 5xx server errors over a specific time window. If that rate exceeds a defined threshold, an alert is triggered.
Here is an example of a PromQL expression for an alert rule:
sum(rate(http_server_requests_seconds_count{status_code=~"5.."}[5m])) / sum(rate(http_server_requests_seconds_count[5m])) > 0.05
Let’s break this down:
- It calculates the per-second rate of requests with a 5xx status code over the last 5 minutes.
- It divides that by the rate of all requests over the same period.
- If this ratio is greater than 5% (
0.05), the alert condition is met.
You would then configure Prometheus’s Alertmanager to send a notification to your on-call team’s preferred channel, ensuring a rapid response.
Step 4: Visualize API Health in Grafana
Finally, connect Grafana to your Prometheus instance as a data source. From there, you can build a comprehensive API health dashboard. Essential panels to include are:
- Overall Error Rate: A graph showing the percentage of 5xx and 4xx errors over time.
- Request Throughput (RPS): The number of requests per second your API is handling.
- p95/p99 Latency: The 95th and 99th percentile response times to identify performance degradation.
- JVM Health: Monitor key metrics like heap memory usage, CPU load, and garbage collection pauses, which are often root causes of 5xx errors.
Actionable Tips for a More Secure and Effective Setup
- Fine-Tune Your Alert Thresholds: Start with a reasonable threshold (e.g., 1-5% error rate) and adjust based on your service’s normal behavior. Setting thresholds too low can lead to alert fatigue, causing teams to ignore important warnings.
- Use Labels Effectively: Prometheus labels are incredibly powerful. Use them to segment metrics by endpoint (
/api/v1/users), HTTP method (GET,POST), or service instance. This allows you to pinpoint exactly which part of your system is failing. - Secure Your Metrics Endpoint: The
/metricsendpoint can expose internal information about your application’s performance and architecture. Do not expose this endpoint to the public internet. Ensure it is only accessible within your private network or protected by firewall rules and authentication. - Go Beyond Metrics: While metrics and alerts are crucial for detecting problems, logs and traces are essential for debugging them. OpenTelemetry can also collect distributed traces, allowing you to follow a request as it travels through multiple services to find the exact source of an error.
By adopting this modern, open-source stack, you can move from a reactive to a proactive stance on API reliability. Gaining deep visibility into your Java application’s performance isn’t just a best practice—it’s essential for protecting your revenue, user trust, and brand reputation.
Source: https://www.fosstechnix.com/monitoring-java-api-5xx-alerts-with-opentelemetry-prometheus-and-grafana/
